import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import Dataset
from torch.utils.data import DataLoader

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

from tqdm import tqdm
from time import sleep
import os
from os import listdir
import matplotlib.image as mpimg
from matplotlib.animation import FuncAnimation as FA
import random

pd.set_option('display.max_columns', 50)
pd.set_option('display.max_rows', 50)

# import time

# # Generate a random seed based on the current time
# seed = int(time.time())
# print("Selected seed:", seed)

seed = 42
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)

folder_path = './CMAPSSData/'

listdir(folder_path)

file_name = 'FD004.txt'

df_train = pd.read_csv(folder_path + 'train_' + file_name, header = None, sep = ' ')
df_test = pd.read_csv(folder_path + 'test_'+file_name, header = None, sep = ' ')
rul_test = pd.read_csv(folder_path + 'RUL_'+file_name, header = None)

for df in [df_train, df_test, rul_test]:
    display(df.head())

col_names = []

col_names.append('unit')
col_names.append('time')

for i in range(1,4):
    col_names.append('os'+str(i))
for i in range(1,22):
    col_names.append('s'+str(i))

df_train = df_train.iloc[:,:-2].copy()
df_train.columns = col_names
display(df_train.head())

df_test = df_test.iloc[:,:-2].copy()
df_test.columns = col_names
display(df_test.head())

units_training = max(df_train['unit'])
units_testing = max(df_test['unit'])
print(f'Units in the training dataset: {units_training}')
print(f'Units in the testing dataset: {units_testing}')
df_train = df_train[df_train['unit'] != 260]
if units_training > units_testing:
    df_train = df_train[df_train['unit'] <= units_testing]
elif units_training < units_testing:
    df_test = df_test[df_test['unit']  <= units_training]

assert max(df_train['unit']) == max(df_test['unit'])
no_units = max(df_train['unit'])
print(f'No of units in training and testing dataset after: {no_units}')

Units in the training dataset: 249
Units in the testing dataset: 248
No of units in training and testing dataset after: 248

MAX_RUL = 125
no_units = min(rul_test.shape[0],max(df_train['unit']))
print(f'units :{no_units}')

units :248

rul_list = []
engine_numbers = no_units
for n in np.arange(1,engine_numbers+1):
    
    time_list = np.array(df_train[df_train['unit'] == n]['time'])
    length = len(time_list)
    rul = list(length - time_list)
    rul = [min(MAX_RUL,x) for x in rul]
    rul_list += rul
    
df_train['rul'] = rul_list

rul_list = []

for n in np.arange(1,engine_numbers+1):
    
    time_list = np.array(df_test[df_test['unit'] == n]['time'])
    length = len(time_list)
    rul_val = rul_test.iloc[n-1].item()
    rul = list(length - time_list + rul_val)
    rul = [min(MAX_RUL,x) for x in rul]
    rul_list += rul

df_test['rul'] = rul_list

for df in [df_train, df_test]:
    display(df.head())

from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler

# Initialize KMeans and StandardScaler
kmeans = KMeans(n_clusters=6, random_state=42)
scaler = StandardScaler()

# Apply clustering for specific datasets
if file_name in ['FD002.txt', 'FD004.txt']:
    # Ensure required columns are present
    if {'os1', 'os2', 'os3'}.issubset(df_train.columns):
        # Select operating conditions and scale them
        operating_conditions = df_train[['os1', 'os2', 'os3']]
        scaled_conditions = scaler.fit_transform(operating_conditions)

        # Apply k-means clustering and assign operation_mode
        df_train['operation_mode'] = kmeans.fit_predict(scaled_conditions)
    else:
        raise ValueError("Columns 'os1', 'os2', and 'os3' are missing in the dataset!")
else:
    print(f"No clustering applied for dataset {file_name}.")

if file_name in ['FD002.txt', 'FD004.txt']:
    df_test['operation_mode'] = kmeans.predict(scaler.transform(df_test[['os1', 'os2', 'os3']]))

for df in [df_train, df_test]:
    display(df.head())

sensor_colums = ['s'+str(i) for i in range(1,22)]
print(sensor_colums)

['s1', 's2', 's3', 's4', 's5', 's6', 's7', 's8', 's9', 's10', 's11', 's12', 's13', 's14', 's15', 's16', 's17', 's18', 's19', 's20', 's21']

##  Z-score Normalization
if file_name in ['FD001.txt', 'FD003.txt']:
    # Dictionary to store mean and std for columns containing 's'
    mean_std_dict = {}

    # Calculate mean and std for 's' columns in df_train
    for c in sensor_colums:
        mean_std_dict[c + '_mean'] = df_train[c].mean()
        mean_std_dict[c + '_std'] = df_train[c].std()

    # Apply standard normalization to df_train
    for c in sensor_colums:
            df_train[c] = (df_train[c] - mean_std_dict[c + '_mean']) / (mean_std_dict[c + '_std'] + 1e-6)

    # Apply standard normalization to df_test using df_train's mean and std
    for c in sensor_colums:
            df_test[c] = (df_test[c] - mean_std_dict[c + '_mean']) / (mean_std_dict[c + '_std'] + 1e-6)

if file_name in ['FD002.txt', 'FD004.txt']:
    # Function to normalize each group based on its operation_mode
    def z_score_normalize_by_mode(group):
        group = group.copy()  # To avoid modifying the original data
        for c in sensor_colums:
            group[c] = (group[c] - group[c].mean()) / (group[c].std() + 1e-6)
        return group

    # Normalize training data
    df_train_normalized = df_train.groupby('operation_mode', group_keys=False).apply(z_score_normalize_by_mode)

    # Normalize test data using train stats for each operation_mode
    for mode in df_train['operation_mode'].unique():
        train_group = df_train[df_train['operation_mode'] == mode]
        test_group = df_test[df_test['operation_mode'] == mode]
        
        for c in sensor_colums:
            # Ensure the column dtype is compatible
            df_test[c] = df_test[c].astype(float)
                
            mean = train_group[c].mean()
            std = train_group[c].std()
                
            df_test.loc[df_test['operation_mode'] == mode, c] = (
                df_test.loc[df_test['operation_mode'] == mode, c] - mean
            ) / (std+1e-6)

    df_train = df_train_normalized.copy()

C:\Users\spiro\AppData\Local\Temp\ipykernel_9692\182794737.py:10: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
  df_train_normalized = df_train.groupby('operation_mode', group_keys=False).apply(z_score_normalize_by_mode)

if file_name in ['FD002.txt', 'FD004.txt']:
    df_train = df_train.drop('operation_mode', axis = 1)
    df_test = df_test.drop('operation_mode', axis = 1)

os_colums = ['os'+str(i) for i in range(1,4)]
print(os_colums)

mean_std_dict = {}
# Calculate mean and std for 's' columns in df_train
for c in os_colums:
    mean_std_dict[c + '_mean'] = df_train[c].mean()
    mean_std_dict[c + '_std'] = df_train[c].std()

# Apply standard normalization to df_train
for c in os_colums:
    df_train[c] = (df_train[c] - mean_std_dict[c + '_mean']) / (mean_std_dict[c + '_std'] + 1e-6)

# Apply standard normalization to df_test using df_train's mean and std
for c in os_colums:
    df_test[c] = (df_test[c] - mean_std_dict[c + '_mean']) / (mean_std_dict[c + '_std'] + 1e-6)

# Display the first few rows of both datasets
for df in [df_train, df_test]:
    display(df.head())

['os1', 'os2', 'os3']

sample = 4
sample_df = df_train[df_train['unit'] == sample].copy()

# Select only sensor columns
n_sensors = len(sensor_colums)
rows = (n_sensors + 2) // 3  # Calculate rows for the grid

# Create subplots
fig, axes = plt.subplots(rows, 3, figsize=(15, 5 * rows))

# Flatten axes for easier iteration
axes = axes.flatten()

# Plot each sensor's data
for j, c in enumerate(sensor_colums):
    axes[j].plot(sample_df['time'], sample_df[c], label='Sensor data')
    axes[j].set_title(c)
    # axes[j].set_xlabel('Time')
    # axes[j].set_ylabel('Value')
    axes[j].legend()

# Hide unused subplots
for ax in axes[n_sensors:]:
    ax.axis('off')

# Adjust layout for better spacing
fig.tight_layout(pad=3.0)
plt.savefig(f'figures\\sensor_of_engine_{sample}_for_dataset_{file_name}.png', dpi=300, bbox_inches='tight')
plt.show()

sensor = 's2'
for sample in range(1,6):
    sample_df = df_train[df_train['unit'] == sample].copy()
    sensordata = sample_df[sensor].to_numpy()
    plt.plot(sensordata, label = "engine "+str(sample))
plt.grid()
plt.legend()
plt.ylabel('Sensor 2')
plt.xlabel('Cycle')
plt.savefig(f'figures\\sensor_{sensor}_of_engines_for_dataset_{file_name}.png', dpi=300, bbox_inches='tight')
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt

# Compute correlation matrix
corr_matrix = df_train.corr()

# Plot heatmap
plt.figure(figsize=(20, 14))
sns.heatmap(corr_matrix, annot=True, cmap="coolwarm", fmt=".2f")
plt.title("Feature Correlation Matrix")
plt.savefig(f'figures\\Feature_Correlation_Matrix_for_dataset_{file_name}.png', dpi=300, bbox_inches='tight')
plt.show()

from sklearn.ensemble import RandomForestRegressor
import pandas as pd

Xt = df_train.iloc[:,2:-1]
yt = df_train.iloc[:,-1]

rf = RandomForestRegressor()
rf.fit(Xt, yt)

# Get feature importance
importance = pd.Series(rf.feature_importances_, index=Xt.columns)
importance = importance.sort_values(ascending=False)

print("Feature Importances:\n", importance)

Feature Importances:
 s11    0.587140
s9     0.095355
s14    0.082954
s4     0.031146
s15    0.027281
s6     0.025749
s13    0.021169
s8     0.015294
s3     0.015219
s12    0.015051
s2     0.015012
s7     0.013798
s21    0.012449
s20    0.011164
s17    0.009922
os1    0.009415
os2    0.006112
s10    0.003264
s16    0.001191
os3    0.000722
s1     0.000593
s5     0.000000
s18    0.000000
s19    0.000000
dtype: float64

# Combine thresholding and sorting
threshold = 1e-3
low_importance_features = importance[importance < threshold].sort_values()
print("Low importance features sorted:\n", low_importance_features)

Low importance features sorted:
 s5     0.000000
s18    0.000000
s19    0.000000
s1     0.000593
os3    0.000722
dtype: float64

df_train[low_importance_features.index].describe()

#Drop os3, s1, s5, s6, s10, s16, s18, s19 from both train and test
drop_cols1 = low_importance_features.index

df_train = df_train.drop(drop_cols1, axis = 1)
df_test = df_test.drop(drop_cols1, axis = 1)

for df in [df_train, df_test]:
    display(df.head())

ratio = 0.8                     #Ratio of training and validation datasets

units = np.arange(1, no_units+1)
no_selected = round(ratio * engine_numbers)
train_units = list(np.random.choice(units,no_selected, replace = False))
val_units = list(set(units) - set(train_units))
print(val_units)


train_data = df_train[df_train['unit'].isin(train_units)].copy()
val_data = df_train[df_train['unit'].isin(val_units)].copy()

[129, 5, 135, 136, 9, 139, 14, 15, 146, 24, 154, 28, 157, 33, 162, 163, 40, 41, 44, 45, 48, 53, 188, 190, 63, 191, 65, 71, 200, 202, 206, 208, 81, 82, 211, 213, 215, 88, 89, 217, 218, 221, 95, 228, 235, 237, 111, 242, 123, 124]

#Smoothing Function: Exponentially Weighted Averages

def smooth(s, b = 0.98):

    v = np.zeros(len(s)+1) #v_0 is already 0.
    bc = np.zeros(len(s)+1)  

    for i in range(1, len(v)): #v_t = 0.95
        v[i] = (b * v[i-1] + (1-b) * s[i-1]) 
        bc[i] = 1 - b**i

    sm = v[1:] / bc[1:]
    
    return sm

# s = [1,2,3,4,5]
# print(s)
# print(f'After Smoothing: {smooth(s)}')

#Smoothing each time series for each engine in both training and test sets
beta = 0.98
# if file_name=='FD002' or file_name=='FD004':
#     beta = 0.8

# Verify data integrity
assert 'unit' in df_train.columns, "The 'unit' column is missing in df_train"
assert 'unit' in df_test.columns, "The 'unit' column is missing in df_test"

def smooth_series(df, beta, unit_col='unit', sensor_prefix='s'):
    """
    Smooths time series for each engine and sensor column in the dataset.
    """
    for col in df.columns:
        if sensor_prefix in col:
            sm_list = []
            for unit in df[unit_col].unique():
                # Get sensor data for the current unit
                unit_data = df[df[unit_col] == unit]
                s = np.array(unit_data[col].copy())
                sm = list(smooth(s, beta))  # Apply smoothing
                sm_list.extend(sm)  # Append smoothed data
            # Check the length match
            if len(sm_list) != len(df):
                raise ValueError(f"Length mismatch for column {col}: sm_list={len(sm_list)}, df={len(df)}")
            # Add smoothed column to DataFrame
            df[col + '_smoothed'] = sm_list
    return df

# Apply smoothing to training and test sets
df_train = smooth_series(df_train, beta)
df_test = smooth_series(df_test, beta)

df_train.columns

Index(['unit', 'time', 'os1', 'os2', 's2', 's3', 's4', 's6', 's7', 's8', 's9',
       's10', 's11', 's12', 's13', 's14', 's15', 's16', 's17', 's20', 's21',
       'rul', 'os1_smoothed', 'os2_smoothed', 's2_smoothed', 's3_smoothed',
       's4_smoothed', 's6_smoothed', 's7_smoothed', 's8_smoothed',
       's9_smoothed', 's10_smoothed', 's11_smoothed', 's12_smoothed',
       's13_smoothed', 's14_smoothed', 's15_smoothed', 's16_smoothed',
       's17_smoothed', 's20_smoothed', 's21_smoothed'],
      dtype='object')

# Set scientific theme
sns.set_theme(style="whitegrid", font="Times New Roman")

# Select unit
sample_unit = 10
sample_df = df_train[df_train['unit'] == sample_unit].copy()

# Extract sensor columns
sensor_cols = [col for col in df_train.columns if col.startswith('s') and '_smoothed' not in col]
n_sensors = len(sensor_cols)

if n_sensors == 0:
    raise ValueError("No valid sensor columns found in the dataset.")

# Define layout for compact visualization
num_cols = 5
num_rows = -(-n_sensors // num_cols)

fig, axes = plt.subplots(num_rows, num_cols, figsize=(9, 2.5 * num_rows), sharex=True, constrained_layout=True)

# Flatten axes
axes = axes.flatten()

# Define colors
colors = {"original": "tab:blue", "smoothed": "#D55E00", "line": "black"}

for j, col in enumerate(sensor_cols):
    axes[j].plot(sample_df['time'], sample_df[col], color=colors["original"], label="Original", lw=0.8)
    
    smoothed_col = f"{col}_smoothed"
    if smoothed_col in sample_df.columns:
        axes[j].plot(sample_df['time'], sample_df[smoothed_col], color=colors["smoothed"], label="Smoothed", lw=0.8)

    # Grid and Labels
    axes[j].set_title(col, fontsize=10)
    axes[j].tick_params(axis='both', labelsize=8)
    axes[j].grid(True, linestyle="--", linewidth=0.5, alpha=0.6)

# Hide unused subplots
for ax in axes[n_sensors:]:
    ax.set_visible(False)

# Global legend for clarity
fig.legend(["Original", "Smoothed"], loc="upper center", ncol=2, bbox_to_anchor=(0.5, -0.02), fontsize=9, frameon=False)

# Save and display
output_dir = "figures"
os.makedirs(output_dir, exist_ok=True)
plt.savefig(os.path.join(output_dir, f"Smoothing_Sensors_{file_name}.png"), dpi=600, bbox_inches="tight", pad_inches=0.05)
plt.show()

#Remove the original series

for c in df_train.columns:
    if ('s' in c) and ('smoothed' not in c):
        df_train[c] = df_train[c+'_smoothed']
        df_train.drop(c+'_smoothed', axis = 1, inplace = True)
        
for c in df_test.columns:
    if ('s' in c) and ('smoothed' not in c):
        df_test[c] = df_test[c+'_smoothed']
        df_test.drop(c+'_smoothed', axis = 1, inplace = True)
        
for df in [df_train, df_test]:
    display(df.head())

print('training set time cycles:')
display(df_train.groupby('unit')['time'].max().describe())
print('test set time cycles:')
display(df_test.groupby('unit')['time'].max().describe())

training set time cycles:

count    248.000000
mean     245.943548
std       73.256391
min      128.000000
25%      189.250000
50%      233.000000
75%      291.250000
max      543.000000
Name: time, dtype: float64

test set time cycles:

count    248.000000
mean     166.185484
std       91.606522
min       19.000000
25%      102.000000
50%      153.500000
75%      218.500000
max      486.000000
Name: time, dtype: float64

n_features = len([c for c in df_train.columns if 's' in c]) + 1 #plus one for time
window = 15
print(f'number of features: {n_features}, window size: {window}')

number of features: 20, window size: 15

train_indices = list(train_data[(train_data['rul'] >= (window - 1)) & (train_data['time'] > 10)].index)
val_indices = list(val_data[(val_data['rul'] >= (window - 1)) & (val_data['time'] > 10)].index)

rul_max = max(df_train['rul'])
df_train['rul'] = df_train['rul'] / rul_max 
df_test['rul'] = df_test['rul'] / rul_max 
rul_max

125

# max_time = max(df_train['time'])
df_train_mean = df_train['time'].mean()
df_train_std = df_train['time'].std()
df_train['time'] = (df_train['time'] - df_train_mean) / df_train_std
df_test['time'] = (df_test['time'] - df_train_mean) / df_train_std
display(df_train)

class data(Dataset):
    
    def __init__(self, list_indices, df_train):
        
        self.indices = list_indices
        self.df_train = df_train
        
    def __len__(self):
        
        return len(self.indices)
    
    def __getitem__(self, idx):
        
        ind = self.indices[idx]
        X_ = self.df_train.iloc[ind : ind + window, :].drop(['unit','rul'], axis = 1).copy().to_numpy()
        y_ = self.df_train.iloc[ind + window - 1]['rul']
        
        return X_, y_
    
train = data(train_indices, df_train)
val = data(val_indices, df_train)

trainloader = DataLoader(train, batch_size = 256, shuffle = True)
valloader = DataLoader(val, batch_size = 128, shuffle = True)

units = np.arange(1, no_units+1)

class test(Dataset):
    
    def __init__(self, units, df_test):
        
        self.units = units
        self.df_test = df_test
        
    def __len__(self):
        
        return len(self.units)
    
    def __getitem__(self, idx):
        
        n = self.units[idx]
        U = self.df_test[self.df_test['unit'] == n].copy()
        X_ = U.reset_index().iloc[-window:,:].drop(['index', 'unit','rul'], axis = 1).copy().to_numpy()
        y_ = U['rul'].min()
        
        return X_, y_
    
test = test(units, df_test)
testloader = DataLoader(test, batch_size = 100)

dataiter = iter(trainloader)
x,y = next(dataiter)
x.shape

torch.Size([256, 15, 20])

def anderson(f, x0, m=5, lam=1e-4, max_iter=50, tol=1e-2, beta=1.0, verbose=False):
    """Improved Anderson acceleration for fixed-point iteration."""
    # Shape parameters
    batch, channels, dim = x0.shape
    
    # Storage for historical X and F
    X = torch.zeros(batch, m, channels * dim, dtype=x0.dtype, device=x0.device)
    F = torch.zeros(batch, m, channels * dim, dtype=x0.dtype, device=x0.device)

    # Initialize X and F with the first two iterations
    X[:, 0], F[:, 0] = x0.view(batch, -1), f(x0).view(batch, -1)
    X[:, 1], F[:, 1] = F[:, 0], f(F[:, 0].view(batch, channels, dim)).view(batch, -1)

    # Prepare the H matrix and y vector for Anderson acceleration
    H = torch.zeros(batch, m + 1, m + 1, dtype=x0.dtype, device=x0.device)
    H[:, 0, 1:] = H[:, 1:, 0] = 1  # First row and column for normalization
    y = torch.zeros(batch, m + 1, 1, dtype=x0.dtype, device=x0.device)
    y[:, 0, 0] = 1  # y vector for the Anderson step

    res = []  # To store residuals

    for k in range(2, max_iter):
        n = min(k, m)  # Number of iterations to consider

        # Compute residual matrix G
        G = F[:, :n] - X[:, :n]

        # Compute the H matrix (Gramian + regularization)
        GTG = torch.bmm(G, G.transpose(1, 2))
        H[:, 1:n+1, 1:n+1] = GTG + lam * torch.eye(n, dtype=x0.dtype, device=x0.device)[None]

        # Solve for alpha using least squares
        try:
            alpha = torch.linalg.solve(H[:, :n+1, :n+1], y[:, :n+1])[:, 1:n+1, 0]  # (batch x n)
        except RuntimeError as e:
            if verbose:
                print(f"Solver failed at iteration {k}: {e}")
            break

        # Update X and F
        X_update = beta * (alpha[:, None] @ F[:, :n])[:, 0] + (1 - beta) * (alpha[:, None] @ X[:, :n])[:, 0]
        idx = k % m
        X[:, idx] = X_update
        F[:, idx] = f(X[:, idx].view(batch, channels, dim)).view(batch, -1)

        # Compute residuals
        residual = (F[:, idx] - X[:, idx]).norm(dim=1) / (1e-5 + X[:, idx].norm(dim=1).clamp(min=1e-5))
        res.append(residual.mean().item())  # Mean residual across batch

        if verbose:
            print(f"Iteration {k}, residual: {res[-1]:.6f}")

        # Check for convergence
        if res[-1] < tol:
            if verbose:
                print(f"Converged at iteration {k}, residual: {res[-1]:.6f}")
            break

    return X[:, idx].view(batch, channels, dim), (res, k)

import torch.autograd as autograd

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print('Using device:', device)

class DEQFixedPoint(nn.Module):
    def __init__(self, f, solver, **kwargs):
        super().__init__()
        self.f = f
        self.solver = solver
        self.kwargs = kwargs

    def forward(self, x):
        # Compute forward pass and re-engage autograd tape
        b, c, f = x.shape
        with torch.no_grad():  # Ensure no gradients for intermediate calculations
            z, self.stats = self.solver(lambda z: self.f(z, x), torch.zeros((b, 4 * c, f), device=device), **self.kwargs)

        z = self.f(z, x)

        # Set z to require gradients before the hook
        z = z.requires_grad_()  # Ensure z requires gradients

        # Set up Jacobian vector product (without additional forward calls)
        z0 = z.clone().detach().requires_grad_()  # Ensure z0 requires gradients
        f0 = self.f(z0, x)

        def backward_hook(grad):
            # Perform the backward pass using the solver
            g, self.backward_res = self.solver(
                lambda y: autograd.grad(f0, z0, y, retain_graph=True)[0] + grad,
                grad, **self.kwargs)
            return g

        # Register the backward hook
        z.register_hook(backward_hook)
        return z, self.stats

Using device: cuda

import torch.optim.lr_scheduler as lr_scheduler

# class ScaledLeakyReLU(nn.Module):
#     def __init__(self, negative_slope=0.1, scaled_slope=0.9):
#         super(ScaledLeakyReLU, self).__init__()
#         self.negative_slope = negative_slope
#         self.scaled_slope = scaled_slope

#     def forward(self, x):
#         return torch.where(x > 0, self.scaled_slope * x, self.negative_slope * x)

class DualInputAttention(nn.Module):
    def __init__(self, channels, feature_size):
        super(DualInputAttention, self).__init__()
        self.feature_size = feature_size
        self.query_proj = nn.Linear(self.feature_size, self.feature_size)
        self.key_proj = nn.Linear(self.feature_size, self.feature_size)
        self.value_proj = nn.Linear(self.feature_size, self.feature_size)
        self.output_proj = nn.Linear(self.feature_size, self.feature_size)
        self.query_proj.weight.data.normal_(0, 0.01)
        self.key_proj.weight.data.normal_(0, 0.01)
        self.value_proj.weight.data.normal_(0, 0.01)
        self.output_proj.weight.data.normal_(0, 0.01)
        
    def forward(self, input1, input2):
        """
        Args:
            input1: Tensor of shape (batch, channels, feature_size)
            input2: Tensor of shape (batch, channels, feature_size)
        
        Returns:
            Tensor of shape (batch, channels, feature_size), attended features.
        """
        # Project inputs
        query = self.query_proj(input1)  # (batch, channels, feature_size)
        key = self.key_proj(input2)     # (batch, channels, feature_size)
        value = self.value_proj(input2) # (batch, channels, feature_size)
        
        # Compute similarity (scaled dot product)
        scores = torch.einsum('bci,bcj->bcij', query, key)  # (batch, channels, feature_size, feature_size)
        scores = scores / (self.feature_size ** 0.5)
        
        # Compute attention weights
        attention_weights = F.softmax(scores, dim=-1)  # (batch, channels, feature_size, feature_size)
        
        # Compute attended values
        attended = torch.einsum('bcij,bcj->bci', attention_weights, value)  # (batch, channels, feature_size)
        
        # Combine with input1
        output = self.output_proj(attended + input1)  # (batch, channels, feature_size)
        
        return output


class DELayer(nn.Module):
    def __init__(self, n_channels, feature_size, kernel_size=3):
        super().__init__()
        num_groups = 4
        self.conv0 = nn.Conv1d(n_channels, 2 * n_channels, kernel_size=1, padding='same', bias=False)
        self.conv1 = nn.Conv1d(2 * n_channels, 2 * n_channels, kernel_size=kernel_size, padding='same', bias=False)
        self.conv2 = nn.Conv1d(2 * n_channels, 2 * n_channels, kernel_size=kernel_size, padding='same', bias=False)
        self.norm1 = nn.GroupNorm(num_groups, 4 * n_channels)
        self.norm2 = nn.GroupNorm(num_groups, 4 * n_channels)
        # self.norm3 = nn.GroupNorm(num_groups, 2 * n_channels)
        # self.scaled_leaky_relu = ScaledLeakyReLU(negative_slope=0.1, scaled_slope=0.9)
        self.attention = DualInputAttention(4 * n_channels, feature_size)
        self.conv0.weight.data.normal_(0, 0.01)
        self.conv1.weight.data.normal_(0, 0.01)
        self.conv2.weight.data.normal_(0, 0.01)

    def forward(self, z0, x):
        # z = self.conv2(self.norm1(F.relu(self.conv1(z0))))
        # out = self.attention (self.norm2(z),self.norm3(x))
        # z = self.norm4(F.relu(out))
        x = F.relu(self.conv0(x))
        x_conv1 = F.relu(self.conv1(x))
        x_conv2 = F.relu(self.conv2(x_conv1))
        x_concat = torch.cat([x_conv1, x_conv2], dim=1)
        x = self.norm1(x_concat)
        # z = self.norm2(z0)
        z = self.norm2(F.relu(self.attention(z0, x)))
        return z

# Define the Bayesian Neural Network with Dropout 
class BNN(nn.Module):
    def __init__(self, initial_channels, hidden_dim, dropout):
        super(BNN, self).__init__()
        self.dropout_value = dropout
        self.fc1 = nn.Linear(4 * initial_channels, 8* hidden_dim)
        self.dropout1 = nn.Dropout(self.dropout_value)  # Dropout for Monte Carlo Dropout
        self.fc2 = nn.Linear(8 * hidden_dim, 4 * hidden_dim)
        self.dropout2 = nn.Dropout(self.dropout_value)
        self.fc3 = nn.Linear(4 * hidden_dim, 1)

    def forward(self, x, training=True):
        x = torch.relu(self.fc1(x))
        x = self.dropout1(x) if training else x   # Apply dropout during training and inference
        x = torch.relu(self.fc2(x))
        x = self.dropout2(x) if training else x   # Apply dropout during training and inference
        return self.fc3(x)

class RegressorModel(nn.Module):
    def __init__(self, initial_channels, feature_size, hidden_dim, dropout):
        super(RegressorModel, self).__init__()

        # Layers
        # self.conv =nn.Conv1d(initial_channels, 2 * initial_channels, kernel_size=1, bias=False)
        # self.norm = nn.GroupNorm(4, 2 * initial_channels)
        self.f = DELayer(n_channels= initial_channels, feature_size=feature_size )
        self.DEQ = DEQFixedPoint(self.f, anderson, tol=1e-4, max_iter=200, beta=1.0)
        self.pool = nn.AdaptiveAvgPool1d(1)
        self.feedforward = BNN(initial_channels, hidden_dim, dropout)

    def forward(self, x, training=True):
        # x = self.norm(F.relu(self.conv(x)))
        x = self.DEQ(x)
        x = self.pool(x[0]).squeeze(-1)             # Apply Deep Equilibrium Model
        # x = torch.flatten(x, start_dim=1)
        output = self.feedforward(x, training)            # Apply feedforward Model for regression
  
        return output

    
learning_rate = 1e-3
init_channels = window
inner_channels = window
n_hidden_units =  window
set_dropout = 0.4

model = RegressorModel(initial_channels=init_channels, feature_size=n_features, hidden_dim=n_hidden_units, dropout=set_dropout ).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) 
scheduler = lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.5)

X= torch.randn((64, window, n_features), device=device)
f = DELayer(n_channels=init_channels, feature_size= n_features).to(device)
DEQ = DEQFixedPoint(f, anderson, tol=1e-4, max_iter=100, beta=0.9).to(device)
out = DEQ(X)[0]
print(out.shape)
out2 = nn.AdaptiveAvgPool1d(1)(out).squeeze(-1)
print(out2.shape)
out3 = model(X)[0]
print(out3.shape)

torch.Size([64, 60, 20])
torch.Size([64, 60])
torch.Size([1])

# def validation(loss_fn, num_samples=100):
#     model.train()  # Enable stochastic behavior (dropout during inference)
#     total_loss = 0.0
#     total_samples = 0

#     with torch.no_grad():
#         for X, y in valloader:
#             X, y = X.to(device).float(), y.to(device).float()

#             stochastic_predictions = []

#             # Perform stochastic forward passes
#             for _ in range(num_samples):
#                 y_pred = model(X, training=True).squeeze()
#                 stochastic_predictions.append(y_pred.cpu().numpy())

#             # Convert to NumPy array
#             stochastic_predictions = np.array(stochastic_predictions)  # Shape: (num_samples, batch_size)

#             # Calculate mean prediction
#             mean_pred = torch.tensor(np.mean(stochastic_predictions, axis=0)).to(device)

#             # Compute batch loss
#             loss = loss_fn(mean_pred, y)
#             total_loss += loss.item() * X.size(0)  # Accumulate the weighted batch loss
#             total_samples += X.size(0)

#     avg_val_loss = total_loss / total_samples  # Calculate average loss
#     return avg_val_loss

def validation(loss_fn):
    model.eval()  # Set the model to evaluation mode
    total_loss = 0.0
    total_samples = 0

    with torch.no_grad():  # Disable gradient computation for validation
        for X, y in valloader:  # Iterate over all batches in the validation loader
            X, y = X.to(device).to(torch.float32), y.to(device).to(torch.float32)
            
            # Use enable_grad for specific models if required
            if isinstance(model, RegressorModel):  # Replace with the DEQ model class
                with torch.enable_grad():
                    y_pred = model(X).squeeze()
            else:
                y_pred = model(X).squeeze()
            
            y = y.squeeze()
            loss = loss_fn(y_pred, y)
            total_loss += loss.item() * X.size(0)  # Accumulate the weighted batch loss
            total_samples += X.size(0)  # Count the total samples processed

    avg_val_loss = total_loss / total_samples  # Calculate average loss
    return avg_val_loss

loss_L1 = nn.L1Loss()
    
def test():
    model.train()  # Enable stochastic behavior (dropout during inference)
    
    total_loss_MSE = 0.0
    total_loss_L1 = 0.0
    total_ASUE = 0.0
    all_stochastic_predictions = []
    all_pred_values = []
    all_true_values = []
    all_uncertainties = []  # Collect uncertainties
    num_samples = 100

    with torch.no_grad():
        for X, y in testloader:
            X, y = X.to(device).float(), y.to(device).float()

            stochastic_predictions = []

            # Perform stochastic forward passes
            for _ in range(num_samples):
                y_pred = model(X, training=True).squeeze()
                stochastic_predictions.append(y_pred.cpu().numpy())
            
            # Convert to NumPy array
            stochastic_predictions = np.array(stochastic_predictions)  # Shape: (num_samples, batch_size)

            # Calculate mean and uncertainty
            mean_pred = torch.tensor(np.mean(stochastic_predictions, axis=0)).to(device)
            uncertainty_batch = np.var(stochastic_predictions, axis=0)  # Variance as uncertainty

            # Compute batch losses
            loss_MSE = torch.mean((mean_pred - y) ** 2).item()
            loss_L1_val = loss_L1(mean_pred, y).item()
            ASUE = torch.mean(torch.relu(y - mean_pred)).item()

            # Aggregate results
            total_loss_MSE += loss_MSE * X.size(0)
            total_loss_L1 += loss_L1_val * X.size(0)
            total_ASUE += ASUE * X.size(0)

            # Collect predictions, true values, and uncertainties
            all_stochastic_predictions.append(stochastic_predictions)
            all_pred_values.append(mean_pred.cpu().numpy())
            all_true_values.append(y.cpu().numpy())
            all_uncertainties.append(uncertainty_batch)
        
    # Average metrics
    total_samples = len(testloader.dataset)
    avg_loss_MSE = total_loss_MSE / total_samples
    avg_loss_L1 = total_loss_L1 / total_samples
    avg_ASUE = total_ASUE / total_samples

    # Concatenate all predictions, uncertainties, and true values
    all_pred_values = np.concatenate([x.flatten() for x in all_pred_values], axis=0)
    all_stochastic_predictions = np.concatenate(all_stochastic_predictions, axis=1)
    all_true_values = np.concatenate(all_true_values, axis=0)
    all_uncertainties = np.concatenate(all_uncertainties, axis=0)  # Ensure consistency

    return avg_loss_MSE, avg_loss_L1, avg_ASUE, all_uncertainties, all_stochastic_predictions, all_pred_values, all_true_values

# Initialize lists for tracking losses
T, V = [], []
epochs = 35

loss_fn = nn.MSELoss()
siterations, sresiduals = [], []
stats = {}

# Define forward hook for DEQ statistics
def forward_hook(module, input, output):
    stats[module] = output

# Register forward hook
hook_handle = model.DEQ.register_forward_hook(forward_hook)

best_val_loss = float('inf')  # Track best validation loss

pbar = tqdm(range(epochs), desc="Training Progress", dynamic_ncols=True)
for epoch in pbar:
    model.train()
    epoch_loss = 0
    sresiduals_list, siterations_list = [], []

    for X, y in trainloader:
        X, y = X.to(device, dtype=torch.float32), y.to(device, dtype=torch.float32)

        # Forward pass
        y_pred = model(X).squeeze()
        y = y.squeeze()
        loss = loss_fn(y_pred, y)
        epoch_loss += loss.item()

        # Backward pass
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # Collect DEQ statistics  
        iterations = stats.get(model.DEQ, (None, [0, 0]))[1] 
        if iterations and len(iterations) >= 2:
            sresiduals_list.append(iterations[0][-1])
            siterations_list.append(iterations[1])

    # Step the scheduler
    scheduler.step()

    # Validation loss
    val_loss = validation(loss_fn)
    T.append(epoch_loss / len(trainloader))
    V.append(val_loss)
 
    # Update tqdm bar
    pbar.set_postfix({'Train Loss': T[-1], 'Val Loss': val_loss})

    # Track DEQ statistics
    sresiduals.append(np.mean(sresiduals_list) if sresiduals_list else 0)
    siterations.append(np.mean(siterations_list) if siterations_list else 0)

    # Save best model
    if val_loss < best_val_loss:
        best_val_loss = val_loss
        torch.save(model.state_dict(), 'best_model.pth')

    print(f'Epoch {epoch + 1}/{epochs} - Train Loss: {T[-1]:.4f}, Val Loss: {V[-1]:.4f}')

# Clean up hook handle
hook_handle.remove()

Training Progress:   3%|▎         | 1/35 [01:23<47:19, 83.52s/it, Train Loss=0.0979, Val Loss=0.0243]

Epoch 1/35 - Train Loss: 0.0979, Val Loss: 0.0243

Training Progress:   6%|▌         | 2/35 [02:48<46:26, 84.44s/it, Train Loss=0.033, Val Loss=0.0152]

Epoch 2/35 - Train Loss: 0.0330, Val Loss: 0.0152

Training Progress:   9%|▊         | 3/35 [04:07<43:48, 82.13s/it, Train Loss=0.0237, Val Loss=0.0131]

Epoch 3/35 - Train Loss: 0.0237, Val Loss: 0.0131

Training Progress:  11%|█▏        | 4/35 [05:31<42:40, 82.60s/it, Train Loss=0.021, Val Loss=0.0121]

Epoch 4/35 - Train Loss: 0.0210, Val Loss: 0.0121

Training Progress:  14%|█▍        | 5/35 [06:55<41:38, 83.27s/it, Train Loss=0.0192, Val Loss=0.0121]

Epoch 5/35 - Train Loss: 0.0192, Val Loss: 0.0121

print(sresiduals)
print(siterations)

[4.629765501090862e-05, 6.0786133999076704e-05, 5.565338161368557e-05, 4.768837594425815e-05, 6.850324595244845e-05, 6.849385082987706e-05, 7.143836879803594e-05, 7.838040940885887e-05, 6.501814759969927e-05, 6.24514488113481e-05, 6.398383843483062e-05, 7.08309207131902e-05, 7.270502892213192e-05, 6.984984097365415e-05, 7.391439238174001e-05, 8.011613920453508e-05, 8.114433523249233e-05, 8.302382069137652e-05, 8.456120009890751e-05, 7.99294200480163e-05, 7.098811853888206e-05, 7.852117562643523e-05, 8.413747449129344e-05, 8.404635754718477e-05, 8.78115205163932e-05, 7.959117566886477e-05, 8.363073995793075e-05, 8.344922634721517e-05, 8.170895713657617e-05, 8.419091284659496e-05, 8.902548151402084e-05, 9.045941484611492e-05, 8.287076220873386e-05, 8.499276242049703e-05, 7.862451677156483e-05]
[9.029069767441861, 9.034883720930232, 8.063953488372093, 8.0, 8.36046511627907, 9.372093023255815, 10.412790697674419, 10.505813953488373, 9.88953488372093, 9.575581395348838, 9.69186046511628, 9.633720930232558, 9.186046511627907, 9.180232558139535, 9.459302325581396, 9.44186046511628, 9.738372093023257, 10.337209302325581, 11.029069767441861, 11.470930232558139, 11.953488372093023, 12.0, 12.05813953488372, 12.05232558139535, 12.13953488372093, 12.093023255813954, 12.203488372093023, 12.372093023255815, 12.75, 12.587209302325581, 13.075581395348838, 13.05232558139535, 13.540697674418604, 13.261627906976743, 13.784883720930232]

torch.save(model.state_dict(), f'saved_models\\DEM_{file_name}')

# Perform testing with Monte Carlo Dropout
mse, l1, asue, uncertainty, all_y_pred, y_pred, y = test()

# Calculate RMSE and scale by RUL max value
rmse = rul_max * np.sqrt(mse)

# Print results
print(f"Test Results:\n"
      f"RMSE: {round(rmse, 2)}\n"
      f"L1 Loss: {round(l1, 2)}\n"
      f"ASUE: {round(asue, 2)}")

# Mean uncertainty
mean_uncertainty = np.mean(uncertainty)
print(f"Mean Uncertainty: {round(mean_uncertainty, 4)}")

# Optional: Visualize predictions, true values, and uncertainties
import matplotlib.pyplot as plt

plt.figure(figsize=(14, 8))

# True vs predicted RUL
plt.plot(range(len(y)), y, label="True Values", c="lightseagreen", marker=".", alpha=0.7)
plt.plot(range(len(y_pred)), y_pred, label="Predicted Values", c="salmon", marker=".", alpha=0.7)

# Uncertainty as error bars
plt.fill_between(
    range(len(y_pred)),
    y_pred - np.sqrt(uncertainty),
    y_pred + np.sqrt(uncertainty),
    color="salmon", alpha=0.2, label="Uncertainty"
)

# Add labels and legend
plt.xlabel("Sample Index", fontsize=14)
plt.ylabel("RUL", fontsize=14)
plt.title("True vs Predicted RUL with Uncertainty", fontsize=16)
plt.legend(fontsize=12)
plt.grid(True)
plt.savefig(f'figures\\Responses_with_Uncertainty_for_dataset_{file_name}.png', dpi=300, bbox_inches='tight')
plt.show()

Test Results:
RMSE: 11.79
L1 Loss: 0.07
ASUE: 0.03
Mean Uncertainty: 0.0034000000450760126

print(y)
print(y_pred)
print(1*(y_pred>y))
print(all_y_pred.shape)

[0.176 0.312 0.856 0.6   1.    0.624 0.752 0.112 0.792 1.    1.    0.056
 0.568 0.84  0.096 1.    1.    0.832 1.    0.656 0.728 0.088 0.208 1.
 0.312 0.736 0.608 0.992 0.512 0.944 0.048 0.176 1.    1.    0.288 0.584
 0.712 0.088 1.    0.08  0.776 0.24  0.336 0.48  0.68  1.    0.272 0.36
 0.192 0.688 0.952 1.    1.    1.    1.    0.536 0.776 0.064 1.    1.
 0.408 0.264 1.    0.368 0.096 1.    0.368 0.368 0.096 0.264 0.12  1.
 0.184 0.712 0.992 1.    0.2   0.592 0.624 0.912 0.768 0.08  1.    1.
 0.92  0.56  0.752 0.448 0.688 0.768 0.4   0.584 1.    1.    1.    0.568
 0.84  0.904 0.296 0.056 0.104 0.176 0.072 0.96  0.8   0.856 0.328 1.
 1.    0.472 0.144 0.528 0.104 0.112 1.    0.104 0.6   0.064 0.872 1.
 0.328 1.    0.184 0.688 1.    0.12  1.    1.    0.96  1.    0.808 0.928
 1.    0.288 0.056 0.976 1.    0.704 1.    1.    1.    0.864 0.424 1.
 0.472 0.8   0.448 1.    0.608 0.456 0.248 0.704 1.    0.272 0.056 1.
 1.    0.048 0.176 0.664 0.656 0.672 0.76  1.    0.888 0.576 0.872 0.696
 1.    1.    1.    0.096 0.064 0.08  0.984 0.824 0.096 0.848 0.096 0.256
 0.296 0.928 0.12  0.08  0.368 1.    0.192 1.    0.448 0.344 1.    0.568
 0.832 0.12  1.    0.712 0.288 0.088 0.736 0.768 0.472 0.104 1.    1.
 1.    0.872 0.928 0.728 0.088 0.704 0.864 0.608 0.112 0.712 1.    0.136
 0.528 1.    0.328 1.    0.584 0.312 0.464 0.112 1.    0.704 1.    1.
 0.96  0.784 0.264 1.    0.88  0.544 0.192 0.6   0.144 0.128 1.    0.784
 1.    0.648 0.944 0.28  1.    1.    0.896 0.208]
[0.2462424  0.22612968 0.9726383  0.74565536 0.98987794 0.5629638
 0.9876078  0.14675337 0.77300537 0.97599757 0.97932214 0.06907845
 0.60648227 0.92693496 0.13410877 1.0029427  0.94054323 0.74515706
 0.98858225 0.68648803 0.6579274  0.09368446 0.22357674 0.9828942
 0.42533615 0.9052162  0.5663383  0.9802998  0.5275922  0.9079585
 0.08195359 0.22274308 0.6973931  0.79651105 0.3384423  0.5541741
 0.60913694 0.11046143 0.7458725  0.1384661  0.7715413  0.34515342
 0.301858   0.37151086 0.83048487 0.95463926 0.3078755  0.44282746
 0.15716332 0.6104344  0.9658088  0.983976   0.9423979  0.94618285
 1.001431   0.501199   0.9581948  0.1046057  0.9684777  0.766831
 0.28662357 0.23676634 0.9742875  0.33788764 0.19063273 0.859799
 0.47100848 0.37116095 0.11618819 0.31927577 0.13510212 0.98332024
 0.24861254 0.9156055  0.9705818  0.93859595 0.25979528 0.6709827
 0.6430121  0.96920294 0.98922735 0.1332146  0.9730394  0.9900741
 0.9792404  0.6133692  0.9566109  0.38244438 0.6167695  0.6194038
 0.4230758  0.5812086  0.98492885 0.95988905 0.96638143 0.6552822
 0.95254976 0.95320314 0.33100057 0.14336959 0.15652771 0.20886818
 0.13409692 0.9935843  0.77358496 0.65991676 0.26668385 0.98532397
 0.9928422  0.5742656  0.13056873 0.8847005  0.1255912  0.21507831
 0.9229842  0.10622708 0.8656443  0.20110266 0.88761157 0.94561636
 0.3301112  0.98461807 0.23270014 0.5954498  0.96808904 0.17934097
 0.98950607 0.9325035  0.97499657 0.8738652  0.6806155  0.95022196
 0.98161185 0.3143995  0.11067381 0.96562856 0.96314186 0.8141581
 0.97733825 0.92926216 0.99643946 0.9783236  0.44274423 0.9624729
 0.5302121  0.9856205  0.42674586 0.8913692  0.5786059  0.40321144
 0.23011269 0.8344016  0.8662287  0.3951003  0.12271074 0.9822601
 0.98576945 0.08965831 0.23458704 0.8308076  0.5976968  0.9018738
 0.64758146 0.9942369  0.98407173 0.88114756 0.98784196 0.71965104
 0.9768777  0.97164696 0.8124566  0.18265249 0.07274331 0.1582355
 0.93094766 0.8057281  0.16951917 0.96500504 0.11236409 0.20663357
 0.18963169 0.9164737  0.13590461 0.12270089 0.37726036 0.96880966
 0.20889229 0.95033556 0.6487601  0.41053978 0.9963639  0.63928187
 0.8139373  0.12891752 0.9971641  0.95218897 0.33437562 0.06403207
 0.63105154 0.6764621  0.5572445  0.0968667  0.9999528  0.99365973
 0.8747377  0.8860685  0.9603575  0.76183    0.13849731 0.6251325
 0.81368667 0.5709297  0.11879976 0.6601202  0.94263464 0.15936175
 0.5716257  0.922511   0.24030536 0.9595926  0.5674498  0.29708254
 0.3931772  0.12726173 0.9858892  0.9569603  0.97806525 0.9719149
 0.9875281  0.9084232  0.23101103 0.94993997 0.9570631  0.4568754
 0.19421495 0.67033285 0.11897636 0.12902626 0.9566067  0.35129988
 0.9625631  0.6133013  0.8180542  0.29817158 0.99673927 0.99112725
 0.74779296 0.299515  ]
[1 0 1 1 0 0 1 1 0 0 0 1 1 1 1 1 0 0 0 1 0 1 1 0 1 1 0 0 1 0 1 1 0 0 1 0 0
 1 0 1 0 1 0 0 1 0 1 1 0 0 1 0 0 0 1 0 1 1 0 0 0 0 0 0 1 0 1 1 1 1 1 0 1 1
 0 0 1 1 1 1 1 1 0 0 1 1 1 0 0 0 1 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 1 0
 1 1 1 0 1 1 1 1 0 1 0 1 0 0 1 0 0 1 0 0 1 0 1 1 0 0 1 0 0 0 1 1 0 1 1 0 0
 0 0 0 1 0 1 1 0 0 1 1 1 0 1 0 0 1 1 1 1 0 0 0 1 1 1 0 0 1 1 1 0 0 0 1 1 1
 0 1 0 1 1 0 1 0 1 0 1 1 0 0 0 1 0 0 0 0 1 1 1 1 0 0 0 1 0 0 1 1 0 0 0 0 0
 0 1 0 1 0 0 1 1 0 0 1 0 1 1 0 1 0 0 0 0 0 1 0 0 0 1]
(100, 248)

print(all_y_pred)

[[0.2956851  0.1307303  0.8932625  ... 1.0380028  0.7612098  0.30560386]
 [0.21882373 0.2946883  1.0059178  ... 0.88635397 0.7102764  0.29325962]
 [0.41497844 0.31902927 0.9113966  ... 0.9928802  0.63424313 0.26908088]
 ...
 [0.2796316  0.22994864 0.96426916 ... 0.9784926  0.7533461  0.32739812]
 [0.24358472 0.27926666 0.97475976 ... 0.96031356 0.7478002  0.28509638]
 [0.30003503 0.25516906 0.95309937 ... 0.96751046 0.77303225 0.27185297]]

# Create a figure and axes
fig, ax = plt.subplots(1, 1, figsize=(14, 8))

# Plot predictions
ax.plot(np.arange(1, y_pred.shape[0] + 1), y_pred, label='Predictions', color='salmon', marker='.')

# Plot true values
ax.plot(np.arange(1, y.shape[0] + 1), y, label='True Values', color='lightseagreen', marker='.')

# Set limits, labels, grid, and legend
ax.set_ylim([0, 1])
ax.set_xlabel('Test Engine Units', fontsize=16)
ax.set_ylabel('RUL', fontsize=16)
ax.grid(True)
ax.legend()
plt.savefig(f'figures\\Responses_without_Uncertainty_for_dataset_{file_name}.png', dpi=300, bbox_inches='tight')
# Show the plot
plt.show()

plt.plot(np.arange(1,len(T)+1), T, label= 'Train loss')
plt.plot(np.arange(1,len(V)+1), V, label = 'Validation loss')
plt.legend()
plt.grid()
plt.xlabel('Epochs')
plt.ylabel('MSE')
plt.savefig(f'figures\\Training_for_dataset_{file_name}.png', dpi=300, bbox_inches='tight')
plt.show

<function matplotlib.pyplot.show(close=None, block=None)>

fig, ax = plt.subplots(figsize = (12,8))

def animate(i):
    ax.clear()
    line1, = ax.plot(np.arange(1,i+1), T[:i], label = 'train_loss')
    line2, = ax.plot(np.arange(1,i+1), V[:i], label = 'val_loss')
    ax.legend()
    ax.grid(True)
    ax.set_xlim(0,101)
    ax.set_ylim(0,4000)
    ax.set_xlabel('epochs')
    ax.set_ylabel('MSE')
                     
    return line1, line2
                     
    
animation = FA(fig, animate, np.arange(1,len(T)+1), interval = 50)

%time animation.save('animation3.gif', writer='imagemagick', fps=20)

plt.close(fig)

MovieWriter imagemagick unavailable; using Pillow instead.

CPU times: total: 21.4 s
Wall time: 22 s

# Count total parameters
total_params = sum(p.numel() for p in model.parameters())
print(f"Total number of parameters: {total_params}")

# Count trainable parameters only
trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f"Trainable parameters: {trainable_params}")

Total number of parameters: 22411
Trainable parameters: 22411

from torchinfo import summary
summary(model, input_size=(64, window, n_features))

===============================================================================================
Layer (type:depth-idx)                        Output Shape              Param #
===============================================================================================
RegressorModel                                [64, 1]                   --
├─DEQFixedPoint: 1-1                          [64, 60, 20]              --
│    └─DELayer: 2-1                           [64, 60, 20]              --
│    │    └─Conv1d: 3-1                       [64, 30, 20]              450
│    │    └─Conv1d: 3-2                       [64, 30, 20]              2,700
│    │    └─Conv1d: 3-3                       [64, 30, 20]              2,700
│    │    └─GroupNorm: 3-4                    [64, 60, 20]              120
│    │    └─DualInputAttention: 3-5           [64, 60, 20]              1,680
│    │    └─GroupNorm: 3-6                    [64, 60, 20]              120
│    └─DELayer: 2-2                           [64, 60, 20]              (recursive)
│    │    └─Conv1d: 3-7                       [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-8                       [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-9                       [64, 30, 20]              (recursive)
│    │    └─GroupNorm: 3-10                   [64, 60, 20]              (recursive)
│    │    └─DualInputAttention: 3-11          [64, 60, 20]              (recursive)
│    │    └─GroupNorm: 3-12                   [64, 60, 20]              (recursive)
│    └─DELayer: 2-3                           [64, 60, 20]              (recursive)
│    │    └─Conv1d: 3-13                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-14                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-15                      [64, 30, 20]              (recursive)
│    │    └─GroupNorm: 3-16                   [64, 60, 20]              (recursive)
│    │    └─DualInputAttention: 3-17          [64, 60, 20]              (recursive)
│    │    └─GroupNorm: 3-18                   [64, 60, 20]              (recursive)
│    └─DELayer: 2-4                           [64, 60, 20]              (recursive)
│    │    └─Conv1d: 3-19                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-20                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-21                      [64, 30, 20]              (recursive)
│    │    └─GroupNorm: 3-22                   [64, 60, 20]              (recursive)
│    │    └─DualInputAttention: 3-23          [64, 60, 20]              (recursive)
│    │    └─GroupNorm: 3-24                   [64, 60, 20]              (recursive)
│    └─DELayer: 2-5                           [64, 60, 20]              (recursive)
│    │    └─Conv1d: 3-25                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-26                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-27                      [64, 30, 20]              (recursive)
│    │    └─GroupNorm: 3-28                   [64, 60, 20]              (recursive)
│    │    └─DualInputAttention: 3-29          [64, 60, 20]              (recursive)
│    │    └─GroupNorm: 3-30                   [64, 60, 20]              (recursive)
│    └─DELayer: 2-6                           [64, 60, 20]              (recursive)
│    │    └─Conv1d: 3-31                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-32                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-33                      [64, 30, 20]              (recursive)
│    │    └─GroupNorm: 3-34                   [64, 60, 20]              (recursive)
│    │    └─DualInputAttention: 3-35          [64, 60, 20]              (recursive)
│    │    └─GroupNorm: 3-36                   [64, 60, 20]              (recursive)
│    └─DELayer: 2-7                           [64, 60, 20]              (recursive)
│    │    └─Conv1d: 3-37                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-38                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-39                      [64, 30, 20]              (recursive)
│    │    └─GroupNorm: 3-40                   [64, 60, 20]              (recursive)
│    │    └─DualInputAttention: 3-41          [64, 60, 20]              (recursive)
│    │    └─GroupNorm: 3-42                   [64, 60, 20]              (recursive)
│    └─DELayer: 2-8                           [64, 60, 20]              (recursive)
│    │    └─Conv1d: 3-43                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-44                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-45                      [64, 30, 20]              (recursive)
│    │    └─GroupNorm: 3-46                   [64, 60, 20]              (recursive)
│    │    └─DualInputAttention: 3-47          [64, 60, 20]              (recursive)
│    │    └─GroupNorm: 3-48                   [64, 60, 20]              (recursive)
│    └─DELayer: 2-9                           [64, 60, 20]              (recursive)
│    │    └─Conv1d: 3-49                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-50                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-51                      [64, 30, 20]              (recursive)
│    │    └─GroupNorm: 3-52                   [64, 60, 20]              (recursive)
│    │    └─DualInputAttention: 3-53          [64, 60, 20]              (recursive)
│    │    └─GroupNorm: 3-54                   [64, 60, 20]              (recursive)
│    └─DELayer: 2-10                          [64, 60, 20]              (recursive)
│    │    └─Conv1d: 3-55                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-56                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-57                      [64, 30, 20]              (recursive)
│    │    └─GroupNorm: 3-58                   [64, 60, 20]              (recursive)
│    │    └─DualInputAttention: 3-59          [64, 60, 20]              (recursive)
│    │    └─GroupNorm: 3-60                   [64, 60, 20]              (recursive)
│    └─DELayer: 2-11                          [64, 60, 20]              (recursive)
│    │    └─Conv1d: 3-61                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-62                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-63                      [64, 30, 20]              (recursive)
│    │    └─GroupNorm: 3-64                   [64, 60, 20]              (recursive)
│    │    └─DualInputAttention: 3-65          [64, 60, 20]              (recursive)
│    │    └─GroupNorm: 3-66                   [64, 60, 20]              (recursive)
│    └─DELayer: 2-12                          [64, 60, 20]              (recursive)
│    │    └─Conv1d: 3-67                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-68                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-69                      [64, 30, 20]              (recursive)
│    │    └─GroupNorm: 3-70                   [64, 60, 20]              (recursive)
│    │    └─DualInputAttention: 3-71          [64, 60, 20]              (recursive)
│    │    └─GroupNorm: 3-72                   [64, 60, 20]              (recursive)
│    └─DELayer: 2-13                          [64, 60, 20]              (recursive)
│    │    └─Conv1d: 3-73                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-74                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-75                      [64, 30, 20]              (recursive)
│    │    └─GroupNorm: 3-76                   [64, 60, 20]              (recursive)
│    │    └─DualInputAttention: 3-77          [64, 60, 20]              (recursive)
│    │    └─GroupNorm: 3-78                   [64, 60, 20]              (recursive)
│    └─DELayer: 2-14                          [64, 60, 20]              (recursive)
│    │    └─Conv1d: 3-79                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-80                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-81                      [64, 30, 20]              (recursive)
│    │    └─GroupNorm: 3-82                   [64, 60, 20]              (recursive)
│    │    └─DualInputAttention: 3-83          [64, 60, 20]              (recursive)
│    │    └─GroupNorm: 3-84                   [64, 60, 20]              (recursive)
│    └─DELayer: 2-15                          [64, 60, 20]              (recursive)
│    │    └─Conv1d: 3-85                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-86                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-87                      [64, 30, 20]              (recursive)
│    │    └─GroupNorm: 3-88                   [64, 60, 20]              (recursive)
│    │    └─DualInputAttention: 3-89          [64, 60, 20]              (recursive)
│    │    └─GroupNorm: 3-90                   [64, 60, 20]              (recursive)
│    └─DELayer: 2-16                          [64, 60, 20]              (recursive)
│    │    └─Conv1d: 3-91                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-92                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-93                      [64, 30, 20]              (recursive)
│    │    └─GroupNorm: 3-94                   [64, 60, 20]              (recursive)
│    │    └─DualInputAttention: 3-95          [64, 60, 20]              (recursive)
│    │    └─GroupNorm: 3-96                   [64, 60, 20]              (recursive)
│    └─DELayer: 2-17                          [64, 60, 20]              (recursive)
│    │    └─Conv1d: 3-97                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-98                      [64, 30, 20]              (recursive)
│    │    └─Conv1d: 3-99                      [64, 30, 20]              (recursive)
│    │    └─GroupNorm: 3-100                  [64, 60, 20]              (recursive)
│    │    └─DualInputAttention: 3-101         [64, 60, 20]              (recursive)
│    │    └─GroupNorm: 3-102                  [64, 60, 20]              (recursive)
├─AdaptiveAvgPool1d: 1-2                      [64, 60, 1]               --
├─BNN: 1-3                                    [64, 1]                   --
│    └─Linear: 2-18                           [64, 120]                 7,320
│    └─Dropout: 2-19                          [64, 120]                 --
│    └─Linear: 2-20                           [64, 60]                  7,260
│    └─Dropout: 2-21                          [64, 60]                  --
│    └─Linear: 2-22                           [64, 1]                   61
===============================================================================================
Total params: 22,411
Trainable params: 22,411
Non-trainable params: 0
Total mult-adds (Units.MEGABYTES): 130.32
===============================================================================================
Input size (MB): 0.08
Forward/backward pass size (MB): 78.43
Params size (MB): 0.09
Estimated Total Size (MB): 78.60
===============================================================================================

model.load_state_dict(torch.load(f'saved_models\\DEM_{file_name}', map_location=torch.device(device)))

C:\Users\spiro\AppData\Local\Temp\ipykernel_9692\4004060783.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  model.load_state_dict(torch.load(f'saved_models\\DEM_{file_name}', map_location=torch.device(device)))

<All keys matched successfully>

def calculate_phm08_score(true_rul, predicted_rul, alpha=13, beta=10):
    """
    Calculate the PHM08 score metric for RUL prediction.

    Parameters:
        true_rul (array-like): Array of true RUL values.
        predicted_rul (array-like): Array of predicted RUL values.
        alpha (float): Scaling factor for early predictions (default=13).
        beta (float): Scaling factor for late predictions (default=10).

    Returns:
        float: Total PHM08 score.
    """
    true_rul = np.array(true_rul)
    predicted_rul = np.array(predicted_rul)

    if true_rul.shape != predicted_rul.shape:
        raise ValueError("Shape mismatch between true and predicted RUL arrays")

    d = (predicted_rul - true_rul) * MAX_RUL

    score = np.where(d < 0, np.exp(-d / alpha) - 1, np.exp(d / beta) - 1)
    return np.sum(score)

num_samples = 100

def test(num_samples=num_samples):
    model.train()  # Enable stochastic behavior (dropout during inference)
    
    total_loss_MSE = 0.0
    total_loss_L1 = 0.0
    total_ASUE = 0.0
    total_phm08_score = 0.0
    all_stochastic_predictions = []
    all_pred_values = []
    all_true_values = []
    all_uncertainties = []  # Collect uncertainties

    with torch.no_grad():
        for X, y in testloader:
            X, y = X.to(device).float(), y.to(device).float()

            stochastic_predictions = []

            # Perform stochastic forward passes
            for _ in range(num_samples):
                y_pred = model(X, training=True).squeeze()
                stochastic_predictions.append(y_pred.cpu().numpy())
            
            # Convert to NumPy array
            stochastic_predictions = np.array(stochastic_predictions)  # Shape: (num_samples, batch_size)

            # Calculate mean and uncertainty with std
            mean_pred = torch.tensor(np.mean(stochastic_predictions, axis=0)).to(device)
            uncertainty_batch = np.std(stochastic_predictions, axis=0)  # Variance as uncertainty

            # Compute batch losses
            loss_MSE = torch.mean((mean_pred - y) ** 2).item()
            loss_L1_val = loss_L1(mean_pred, y).item()
            ASUE = torch.mean(torch.relu(y - mean_pred)).item()

            # Compute PHM08 score
            phm08_score = calculate_phm08_score(y.cpu().numpy(), mean_pred.cpu().numpy())

            # Aggregate results
            total_loss_MSE += loss_MSE * X.size(0)
            total_loss_L1 += loss_L1_val * X.size(0)
            total_ASUE += ASUE * X.size(0)
            total_phm08_score += phm08_score

            # Collect predictions, true values, and uncertainties
            all_stochastic_predictions.append(stochastic_predictions)
            all_pred_values.append(mean_pred.cpu().numpy())
            all_true_values.append(y.cpu().numpy())
            all_uncertainties.append(uncertainty_batch)
        
    # Average metrics
    total_samples = len(testloader.dataset)
    avg_loss_MSE = total_loss_MSE / total_samples
    avg_loss_L1 = total_loss_L1 / total_samples
    avg_ASUE = total_ASUE / total_samples
    avg_phm08_score = total_phm08_score

    # Concatenate all predictions, uncertainties, and true values
    all_pred_values = np.concatenate([x.flatten() for x in all_pred_values], axis=0)
    all_stochastic_predictions = np.concatenate(all_stochastic_predictions, axis=1)
    all_true_values = np.concatenate(all_true_values, axis=0)
    all_uncertainties = np.concatenate(all_uncertainties, axis=0)  # Ensure consistency

    return avg_loss_MSE, avg_loss_L1, avg_ASUE, avg_phm08_score, all_uncertainties, all_stochastic_predictions, all_pred_values, all_true_values

# Perform testing with Monte Carlo Dropout
mse, l1, asue, score, uncertainty, all_y_pred, y_pred, y = test()

# Calculate RMSE and scale by RUL max value
rmse = rul_max * np.sqrt(mse)

# Print results
print(f"Test Results:\n"
      f"RMSE: {round(rmse, 2)}\n"
      f"Score: {round(score, 2)}\n"
      f"L1 Loss: {round(l1, 2)}\n"
      f"ASUE: {round(asue, 2)}")

# Mean uncertainty
mean_uncertainty = np.mean(uncertainty)
print(f"Mean Uncertainty: {round(mean_uncertainty, 4)}")

# Optional: Visualize predictions, true values, and uncertainties
import matplotlib.pyplot as plt

plt.figure(figsize=(14, 8))

# True vs predicted RUL
plt.plot(range(len(y)), y, label="True Values", c="lightseagreen", marker=".", alpha=0.7)
plt.plot(range(len(y_pred)), y_pred, label="Predicted Values", c="salmon", marker=".", alpha=0.7)

# Uncertainty as error bars
plt.fill_between(
    range(len(y_pred)),
    y_pred - uncertainty,
    y_pred + uncertainty,
    color="salmon", alpha=0.2, label="Uncertainty"
)

# Add labels and legend
plt.xlabel("Sample Index", fontsize=14)
plt.ylabel("RUL", fontsize=14)
plt.title("True vs Predicted RUL with Uncertainty", fontsize=16)
plt.legend(fontsize=12)
plt.grid(True)
plt.savefig(f'figures\\Responses_with_Uncertainty_for_dataset_{file_name}.png', dpi=300, bbox_inches='tight')
plt.show()

Test Results:
RMSE: 11.82
Score: 648.08
L1 Loss: 0.07
ASUE: 0.03
Mean Uncertainty: 0.05570000037550926

values = np.array([y, y_pred, uncertainty]).T
df_values = pd.DataFrame(values)
values = df_values.sort_values(0, ascending=False).to_numpy()

y, y_pred, uncertainty = np.array(values).T

# y, y_pred, uncertainty = MAX_RUL * np.array(values).T
# uncertainty = MAX_RUL * uncertainty

plt.hist(uncertainty, bins=50, color="blue", alpha=0.7, label="Uncertainty Distribution")
plt.xlabel("Uncertainty")
plt.ylabel("Frequency")
plt.title("Histogram of Prediction Uncertainty")
plt.legend()
plt.grid(True)
plt.show()

# confidence = 0.99  # Confidence level
# zeta = 2.576

# # Calculate confidence interval
# standard_error = uncertainty / np.sqrt(num_samples)  # Standard error of the mean
# margin_of_error = zeta * standard_error  # Margin of error

confidence = 0.95  # Confidence level
margin_of_error = confidence * uncertainty  # Margin of error

plt.figure(figsize=(14, 8))

# True Values (more prominent line)
plt.plot(range(len(y)), y, label="True RUL", c="lightseagreen", marker="o", linewidth=2, alpha=0.8)

# Predicted Values (smaller, less opaque points)
plt.scatter(range(len(y_pred)), y_pred, label="Predicted RUL", c="salmon", edgecolor="black", s=30, alpha=0.7, zorder=3)

# Vertical Lines (improved clarity)
for i in range(len(y)):
    plt.plot([i, i], [y_pred[i], y[i]], c="gray", linestyle="--", linewidth=0.8, alpha=0.7)

# Confidence Interval (adjusted opacity, more descriptive label)
plt.fill_between(
    range(len(y_pred)),
    y_pred - margin_of_error,
    y_pred + margin_of_error,
    color="salmon",
    alpha=0.3,  # Reduced opacity
    label=f"{int(confidence * 100)}% Prediction Interval"  # More accurate label
)

# Labels and Title (improved clarity)
plt.xlabel("Testing Sample Index", fontsize=14)  # more context
plt.ylabel("Remaining Useful Life (cycles)", fontsize=14)  # Full RUL
plt.title("Comparison of True and Predicted Remaining Useful Life (RUL)", fontsize=16)  # more descriptive title

# Legend and Grid
plt.legend(fontsize=12)
plt.grid(True, alpha=0.5)  # reduced grid alpha

# Save and Show
plt.savefig(f'figures/True_vs_PredictedRUL_with_Confidence_Interval_{file_name}.png', dpi=300, bbox_inches="tight")
plt.show()

import matplotlib as mpl
mpl.rcParams['font.family'] = 'serif'  # Use serif font for better readability
mpl.rcParams['font.size'] = 12  # Consistent font size
mpl.rcParams['axes.linewidth'] = 1  # Thicker axes lines
mpl.rcParams['xtick.major.size'] = 5  # Ticks adjustments
mpl.rcParams['ytick.major.size'] = 5
mpl.rcParams['xtick.major.pad'] = 5
mpl.rcParams['ytick.major.pad'] = 5
mpl.rcParams['axes.labelsize'] = 14
mpl.rcParams['axes.titlesize'] = 16
mpl.rcParams['legend.fontsize'] = 12
mpl.rcParams['figure.dpi'] = 300  # High DPI for publication quality
mpl.rcParams['savefig.bbox'] = 'tight'  # Tight bounding box
mpl.rcParams['axes.grid'] = True  # Enable grid by default
mpl.rcParams['grid.alpha'] = 0.5 # Grid alpha
mpl.rcParams['lines.linewidth'] = 1.5 # Line width
mpl.rcParams['scatter.marker'] = 'o' # Marker

plt.figure(figsize=(8, 6)) # Adjust figure size for publication

# True Values (more prominent line)
plt.plot(range(len(y)), y, label="True RUL", c="lightseagreen", linewidth=2)

# Predicted Values (smaller scatter points)
plt.scatter(range(len(y_pred)), y_pred, label="Predicted RUL", c="salmon", edgecolor="black", alpha=0.7, zorder=3, s=15) # reduced scatter point size to 15

# Vertical Lines (improved clarity)
for i in range(len(y)):
    plt.plot([i, i], [y_pred[i], y[i]], c="gray", linestyle="--", linewidth=0.8, alpha=0.7)

# Confidence Interval (adjusted opacity, more descriptive label)
plt.fill_between(
    range(len(y_pred)),
    y_pred - margin_of_error,
    y_pred + margin_of_error,
    color="salmon",
    alpha=0.3,  # Reduced opacity
    label=f"{int(confidence * 100)}% Prediction Interval"  # More accurate label
)

# Labels and Title (improved clarity)
dataset_name = file_name[:-4]
plt.xlabel("Testing Sample Index") # Removed unnecessary context from label
plt.ylabel("Remaining Useful Life (RUL)") # Removed unnecessary context from label
plt.title(f"Comparison of True and Predicted RUL for {dataset_name}") # Removed unnecessary context from title

# Legend and Grid
plt.legend()

# Save and Show
plt.savefig(f'figures/True_vs_PredictedRUL_{file_name}.png')
plt.show()

# Calculate errors and mean predictions
errors = np.abs(y_pred.mean(axis=0) - y)

# Scatter plot with enhancements
plt.figure(figsize=(8, 6))
sc = plt.scatter(uncertainty, errors, c=errors, cmap='viridis', alpha=0.7, edgecolor='k')

# Add colorbar
cbar = plt.colorbar(sc)
cbar.set_label('Error Magnitude')

# Add grid and labels
plt.grid(alpha=0.3)
plt.xlabel('Uncertainty', fontsize=12)
plt.ylabel('Error', fontsize=12)
plt.title('Uncertainty vs. Error', fontsize=14)

# Optional: Fit a trend line
z = np.polyfit(uncertainty, errors, 1)
p = np.poly1d(z)

plt.plot(uncertainty, p(uncertainty), color='red', linestyle='--', label='Trend Line')

# Add legend
plt.legend()

# Show the plot
plt.tight_layout()
plt.savefig(f'figures\\UncertaintyVSError_{file_name}.png', dpi=300, bbox_inches='tight')
plt.show()


# Compute statistics
correlation = np.corrcoef(uncertainty, errors)[0, 1]
print(f"Correlation between uncertainty and error: {correlation:.2f}")

# Evaluate performance in high/low uncertainty regions
threshold = np.percentile(uncertainty, 75)  # Upper 25% uncertainty
high_uncertainty_mask = uncertainty > threshold
low_uncertainty_mask = uncertainty <= threshold

high_uncertainty_error = errors[high_uncertainty_mask].mean()
low_uncertainty_error = errors[low_uncertainty_mask].mean()

print(f"Mean error in high uncertainty regions: {high_uncertainty_error:.2f}")
print(f"Mean error in low uncertainty regions: {low_uncertainty_error:.2f}")

Correlation between uncertainty and error: 0.79
Mean error in high uncertainty regions: 0.49
Mean error in low uncertainty regions: 0.25

model.load_state_dict(torch.load(f'saved_models\\DEM_{file_name}', map_location=torch.device(device)))

C:\Users\spiro\AppData\Local\Temp\ipykernel_9692\4004060783.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  model.load_state_dict(torch.load(f'saved_models\\DEM_{file_name}', map_location=torch.device(device)))

<All keys matched successfully>

def calculate_phm08_score(true_rul, predicted_rul, alpha=13, beta=10):
    """
    Calculate the PHM08 score metric for RUL prediction.

    Parameters:
        true_rul (array-like): Array of true RUL values.
        predicted_rul (array-like): Array of predicted RUL values.
        alpha (float): Scaling factor for early predictions (default=13).
        beta (float): Scaling factor for late predictions (default=10).

    Returns:
        float: Total PHM08 score.
    """
    true_rul = np.array(true_rul)
    predicted_rul = np.array(predicted_rul)

    if true_rul.shape != predicted_rul.shape:
        raise ValueError("Shape mismatch between true and predicted RUL arrays")

    d = (predicted_rul - true_rul) * MAX_RUL

    score = np.where(d < 0, np.exp(-d / alpha) - 1, np.exp(d / beta) - 1)
    return np.sum(score)

def test_without_dropout():
    model.eval()  # Disable stochastic behavior (dropout during inference)

    total_loss_MSE = 0.0
    total_loss_L1 = 0.0
    total_ASUE = 0.0
    total_phm08_score = 0.0
    all_pred_values = []
    all_true_values = []

    with torch.no_grad():
        for X, y in testloader:
            X, y = X.to(device).float(), y.to(device).float()

            # Perform a single deterministic forward pass
            y_pred = model(X).squeeze()

            # Compute batch losses
            loss_MSE = torch.mean((y_pred - y) ** 2).item()
            loss_L1_val = loss_L1(y_pred, y).item()
            ASUE = torch.mean(torch.relu(y - y_pred)).item()

            # Compute PHM08 score
            phm08_score = calculate_phm08_score(y.cpu().numpy(), y_pred.cpu().numpy())

            # Aggregate results
            total_loss_MSE += loss_MSE * X.size(0)
            total_loss_L1 += loss_L1_val * X.size(0)
            total_ASUE += ASUE * X.size(0)
            total_phm08_score += phm08_score

            # Collect predictions and true values
            all_pred_values.append(y_pred.cpu().numpy())
            all_true_values.append(y.cpu().numpy())

    # Average metrics
    total_samples = len(testloader.dataset)
    avg_loss_MSE = total_loss_MSE / total_samples
    avg_loss_L1 = total_loss_L1 / total_samples
    avg_ASUE = total_ASUE / total_samples
    avg_phm08_score = total_phm08_score

    # Concatenate all predictions and true values
    all_pred_values = np.concatenate([x.flatten() for x in all_pred_values], axis=0)
    all_true_values = np.concatenate(all_true_values, axis=0)

    return avg_loss_MSE, avg_loss_L1, avg_ASUE, avg_phm08_score, all_pred_values, all_true_values

# Perform testing without Monte Carlo Dropout
mse, l1, asue, score, y_pred, y = test_without_dropout()

# Calculate RMSE and scale by RUL max value
rmse = rul_max * np.sqrt(mse)

# Print results
print(f"Test Results:\n"
      f"RMSE: {rmse:.2f}\n"
      f"Score: {score:.2f}\n"
      f"L1 Loss: {l1:.2f}\n"
      f"ASUE: {asue:.3f}")

Test Results:
RMSE: 11.81
Score: 648.47
L1 Loss: 0.07
ASUE: 0.029

# Perform testing with Monte Carlo Dropout
mse, l1, asue, score, uncertainty, all_y_pred, y_pred, y = test()

loss_MSE = np.mean((y_pred - y) ** 2).item()
rmse = rul_max * np.sqrt(mse)
print(f'RMSE:{rmse:.2f}')

RMSE:11.75

all_y_pred_mean = all_y_pred.mean(axis=0)
weights = abs(all_y_pred - all_y_pred_mean)
weights = np.exp(-weights)
# weights = 1 / weights
weights /= weights.sum(axis=0)
weighted_predictions = np.sum(weights * all_y_pred, axis=0)

mse = np.mean((weighted_predictions - y) ** 2).item()
rmse = rul_max * np.sqrt(mse)
print(f'RMSE:{rmse:.2f}')

RMSE:11.76

# Define a threshold based on deviations (e.g., 1.5x the mean deviation)
threshold = 1.5 * weights.mean(axis=0)

# Mask predictions with deviations exceeding the threshold
valid_predictions = np.where(weights <= threshold, all_y_pred, np.nan)

# Compute predictions as the mean of valid predictions
weighted_predictions = np.nanmean(valid_predictions, axis=0)

mse = np.mean((weighted_predictions - y) ** 2).item()
rmse = rul_max * np.sqrt(mse)
print(f'RMSE:{rmse:.2f}')

RMSE:11.75

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27
0	1	1	42.0049	0.8400	100.0	445.00	549.68	1343.43	1112.93	3.91	5.70	137.36	2211.86	8311.32	1.01	41.69	129.78	2387.99	8074.83	9.3335	0.02	330	2212	100.00	10.62	6.3670	NaN	NaN
1	1	2	20.0020	0.7002	100.0	491.19	606.07	1477.61	1237.50	9.35	13.61	332.10	2323.66	8713.60	1.07	43.94	312.59	2387.73	8046.13	9.1913	0.02	361	2324	100.00	24.37	14.6552	NaN	NaN
2	1	3	42.0038	0.8409	100.0	445.00	548.95	1343.12	1117.05	3.91	5.69	138.18	2211.92	8306.69	1.01	41.66	129.62	2387.97	8066.62	9.4007	0.02	329	2212	100.00	10.48	6.4213	NaN	NaN
3	1	4	42.0000	0.8400	100.0	445.00	548.70	1341.24	1118.03	3.91	5.70	137.98	2211.88	8312.35	1.02	41.68	129.80	2388.02	8076.05	9.3369	0.02	328	2212	100.00	10.54	6.4176	NaN	NaN
4	1	5	25.0063	0.6207	60.0	462.54	536.10	1255.23	1033.59	7.05	9.00	174.82	1915.22	7994.94	0.93	36.48	164.11	2028.08	7865.80	10.8366	0.02	305	1915	84.93	14.03	8.6754	NaN	NaN

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27
0	1	1	20.0072	0.7000	100.0	491.19	606.67	1481.04	1227.81	9.35	13.60	332.52	2323.67	8704.98	1.07	43.83	313.03	2387.78	8048.98	9.2229	0.02	362	2324	100.00	24.31	14.7007	NaN	NaN
1	1	2	24.9984	0.6200	60.0	462.54	536.22	1256.17	1031.48	7.05	9.00	174.46	1915.21	7999.94	0.93	36.11	163.61	2028.09	7863.46	10.8632	0.02	306	1915	84.93	14.36	8.5748	NaN	NaN
2	1	3	42.0000	0.8420	100.0	445.00	549.23	1340.13	1105.88	3.91	5.69	137.34	2211.93	8305.38	1.01	41.52	129.98	2387.95	8071.13	9.3960	0.02	328	2212	100.00	10.39	6.4365	NaN	NaN
3	1	4	42.0035	0.8402	100.0	445.00	549.19	1339.70	1107.26	3.91	5.69	137.23	2211.89	8301.00	1.01	41.73	129.48	2387.90	8078.89	9.3594	0.02	328	2212	100.00	10.56	6.2367	NaN	NaN
4	1	5	35.0079	0.8400	100.0	449.44	555.10	1353.04	1117.80	5.48	7.97	192.94	2222.71	8331.05	1.02	41.32	181.82	2387.87	8057.83	9.3030	0.02	333	2223	100.00	14.85	8.9326	NaN	NaN

	unit	time	os1	os2	os3	s1	s2	s3	s4	s5	s6	s7	s8	s9	s10	s11	s12	s13	s14	s15	s16	s17	s18	s19	s20	s21
0	1	1	42.0049	0.8400	100.0	445.00	549.68	1343.43	1112.93	3.91	5.70	137.36	2211.86	8311.32	1.01	41.69	129.78	2387.99	8074.83	9.3335	0.02	330	2212	100.00	10.62	6.3670
1	1	2	20.0020	0.7002	100.0	491.19	606.07	1477.61	1237.50	9.35	13.61	332.10	2323.66	8713.60	1.07	43.94	312.59	2387.73	8046.13	9.1913	0.02	361	2324	100.00	24.37	14.6552
2	1	3	42.0038	0.8409	100.0	445.00	548.95	1343.12	1117.05	3.91	5.69	138.18	2211.92	8306.69	1.01	41.66	129.62	2387.97	8066.62	9.4007	0.02	329	2212	100.00	10.48	6.4213
3	1	4	42.0000	0.8400	100.0	445.00	548.70	1341.24	1118.03	3.91	5.70	137.98	2211.88	8312.35	1.02	41.68	129.80	2388.02	8076.05	9.3369	0.02	328	2212	100.00	10.54	6.4176
4	1	5	25.0063	0.6207	60.0	462.54	536.10	1255.23	1033.59	7.05	9.00	174.82	1915.22	7994.94	0.93	36.48	164.11	2028.08	7865.80	10.8366	0.02	305	1915	84.93	14.03	8.6754

	unit	time	os1	os2	os3	s1	s2	s3	s4	s5	s6	s7	s8	s9	s10	s11	s12	s13	s14	s15	s16	s17	s18	s19	s20	s21
0	1	1	20.0072	0.7000	100.0	491.19	606.67	1481.04	1227.81	9.35	13.60	332.52	2323.67	8704.98	1.07	43.83	313.03	2387.78	8048.98	9.2229	0.02	362	2324	100.00	24.31	14.7007
1	1	2	24.9984	0.6200	60.0	462.54	536.22	1256.17	1031.48	7.05	9.00	174.46	1915.21	7999.94	0.93	36.11	163.61	2028.09	7863.46	10.8632	0.02	306	1915	84.93	14.36	8.5748
2	1	3	42.0000	0.8420	100.0	445.00	549.23	1340.13	1105.88	3.91	5.69	137.34	2211.93	8305.38	1.01	41.52	129.98	2387.95	8071.13	9.3960	0.02	328	2212	100.00	10.39	6.4365
3	1	4	42.0035	0.8402	100.0	445.00	549.19	1339.70	1107.26	3.91	5.69	137.23	2211.89	8301.00	1.01	41.73	129.48	2387.90	8078.89	9.3594	0.02	328	2212	100.00	10.56	6.2367
4	1	5	35.0079	0.8400	100.0	449.44	555.10	1353.04	1117.80	5.48	7.97	192.94	2222.71	8331.05	1.02	41.32	181.82	2387.87	8057.83	9.3030	0.02	333	2223	100.00	14.85	8.9326

	unit	time	os1	os2	os3	s1	s2	s3	s4	s5	s6	s7	s8	s9	s10	s11	s12	s13	s14	s15	s16	s17	s18	s19	s20	s21	rul
0	1	1	42.0049	0.8400	100.0	445.00	549.68	1343.43	1112.93	3.91	5.70	137.36	2211.86	8311.32	1.01	41.69	129.78	2387.99	8074.83	9.3335	0.02	330	2212	100.00	10.62	6.3670	125
1	1	2	20.0020	0.7002	100.0	491.19	606.07	1477.61	1237.50	9.35	13.61	332.10	2323.66	8713.60	1.07	43.94	312.59	2387.73	8046.13	9.1913	0.02	361	2324	100.00	24.37	14.6552	125
2	1	3	42.0038	0.8409	100.0	445.00	548.95	1343.12	1117.05	3.91	5.69	138.18	2211.92	8306.69	1.01	41.66	129.62	2387.97	8066.62	9.4007	0.02	329	2212	100.00	10.48	6.4213	125
3	1	4	42.0000	0.8400	100.0	445.00	548.70	1341.24	1118.03	3.91	5.70	137.98	2211.88	8312.35	1.02	41.68	129.80	2388.02	8076.05	9.3369	0.02	328	2212	100.00	10.54	6.4176	125
4	1	5	25.0063	0.6207	60.0	462.54	536.10	1255.23	1033.59	7.05	9.00	174.82	1915.22	7994.94	0.93	36.48	164.11	2028.08	7865.80	10.8366	0.02	305	1915	84.93	14.03	8.6754	125

2. Model Building and Training¶

3. Prediction on Test Set¶

4. Prediction on Test Set Without Dropout in the Inference¶

5. Methods for Weighting Predictions by Uncertainty¶

	unit	time	os1	os2	os3	s1	s2	s3	s4	s5	s6	s7	s8	s9	s10	s11	s12	s13	s14	s15	s16	s17	s19	s20	s21	rul
0	1	1	1.218323	0.864989	0.418618	0.000000e+00	0.432664	-1.388852	-1.286119	1.776357e-09	-0.760397	-1.517470	-0.606409	-0.878364	-1.792634	-1.211197	-1.096189	-0.384740	-0.946674	0.020721	0.000000	-0.330196	0.000000e+00	-0.024111	-0.079174	125
1	1	2	-0.270016	0.415154	0.418618	1.136868e-07	-2.477972	-0.891902	-1.361476	1.776357e-09	-1.286994	-1.471435	-1.667478	-0.880680	-1.411996	-1.422723	-1.479263	-1.914105	-1.374161	0.009604	-0.438767	-2.282832	0.000000e+00	-0.939500	-0.633525	125
2	1	3	1.218249	0.867885	0.418618	0.000000e+00	-1.139180	-1.435904	-0.828803	1.776357e-09	-1.698290	-0.724447	-0.355415	-1.128448	-1.792634	-1.315582	-1.263988	-0.462217	-1.435947	0.952683	0.000000	-0.933051	0.000000e+00	-1.203853	0.682282	125
3	1	4	1.217992	0.864989	0.418618	0.000000e+00	-1.677483	-1.721248	-0.720023	1.776357e-09	-0.760397	-0.917867	-0.522745	-0.822730	-0.361048	-1.245992	-1.075214	-0.268524	-0.873968	0.067873	0.000000	-1.535907	0.000000e+00	-0.698249	0.630397	125
4	1	5	0.068489	0.159347	-2.388771	5.684342e-08	-1.658357	-0.939885	-1.768343	1.776357e-09	-1.512372	-0.911462	-0.911903	-1.444806	-5.404283	-0.866921	-0.804348	-1.032676	-1.037340	-0.383545	0.000000	-1.278025	-2.842171e-08	-2.186010	1.153945	125

	unit	time	os1	os2	os3	s1	s2	s3	s4	s5	s6	s7	s8	s9	s10	s11	s12	s13	s14	s15	s16	s17	s19	s20	s21	rul
0	1	1	-0.269665	0.414510	0.418618	1.136868e-07	-1.296849	-0.374676	-2.451586	1.776357e-09	-1.721582	-1.273268	-1.618666	-1.364460	-1.411996	-1.811782	-1.258376	-1.676665	-1.186464	0.495988	-0.438767	-1.687027	0.000000e+00	-1.284064	-0.196332	125
1	1	2	0.067955	0.157094	-2.388771	5.684342e-08	-1.347352	-0.778231	-2.050746	1.776357e-09	-1.512372	-1.257821	-0.959334	-1.068502	-5.404283	-2.402610	-1.326402	-0.987942	-1.236464	-0.018966	0.000000	-0.579993	-2.842171e-08	0.388681	-0.162087	125
2	1	3	1.217992	0.871424	0.418618	0.000000e+00	-0.536281	-1.889723	-2.068664	1.776357e-09	-1.698290	-1.536813	-0.313583	-1.199206	-1.792634	-1.802711	-0.886439	-0.539694	-1.167174	0.887501	0.000000	-1.535907	0.000000e+00	-1.962258	0.895434	125
3	1	4	1.218228	0.865633	0.418618	0.000000e+00	-0.622410	-1.954988	-1.915485	1.776357e-09	-1.698290	-1.643194	-0.480912	-1.435787	-1.792634	-1.072017	-1.410813	-0.733387	-0.704719	0.379915	0.000000	-1.535907	0.000000e+00	-0.529715	-1.906389	125
4	1	5	0.745026	0.864989	0.418618	1.705302e-07	-0.959379	-1.814420	-1.210958	-1.776357e-09	-1.580330	-1.437290	-1.294164	-1.399778	-0.507686	-2.020050	-1.275919	-0.976771	-0.995511	0.208434	0.000000	-0.450906	0.000000e+00	-0.240601	0.035089	125

	s5	s18	s19	s1	os3
count	6.099400e+04	60994.0	6.099400e+04	6.099400e+04	6.099400e+04
mean	1.728769e-10	0.0	-4.238047e-09	3.369189e-08	-3.572867e-16
std	2.019544e-09	0.0	1.012389e-08	8.695509e-08	9.999999e-01
min	-3.552714e-09	0.0	-2.842171e-08	-1.136868e-07	-2.388771e+00
25%	-1.776357e-09	0.0	0.000000e+00	0.000000e+00	4.186184e-01
50%	1.776357e-09	0.0	0.000000e+00	0.000000e+00	4.186184e-01
75%	1.776357e-09	0.0	0.000000e+00	1.136868e-07	4.186184e-01
max	1.776357e-09	0.0	0.000000e+00	1.705302e-07	4.186184e-01

	unit	time	os1	os2	s2	s3	s4	s6	s7	s8	s9	s10	s11	s12	s13	s14	s15	s16	s17	s20	s21	rul
0	1	-1.484104	1.218323	0.864989	0.432664	-1.388852	-1.286119	-0.760397	-1.517470	-0.606409	-0.878364	-1.792634	-1.211197	-1.096189	-0.384740	-0.946674	0.020721	0.000000	-0.330196	-0.024111	-0.079174	1.000
1	1	-1.472974	0.466637	0.637800	-1.037355	-1.137867	-1.324178	-1.026355	-1.494220	-1.142303	-0.879534	-1.600393	-1.318028	-1.289661	-1.157146	-1.162577	0.015106	-0.221599	-1.316376	-0.486428	-0.359149	1.000
2	1	-1.461843	0.722252	0.716049	-1.071985	-1.239226	-1.155706	-1.254874	-1.232428	-0.874690	-0.964187	-1.665772	-1.317196	-1.280930	-0.920808	-1.255547	0.333967	-0.146236	-1.186011	-0.730417	-0.004969	1.000
3	1	-1.450713	0.849968	0.754420	-1.227977	-1.363408	-1.043463	-1.127483	-1.151389	-0.784020	-0.927744	-1.329641	-1.298852	-1.227932	-0.752763	-1.157242	0.265414	-0.108562	-1.276153	-0.722130	0.158718	1.000
4	1	-1.439583	0.687294	0.630549	-1.317566	-1.275247	-1.194355	-1.207603	-1.101445	-0.810640	-1.035376	-2.177825	-1.208941	-1.139758	-0.811030	-1.132283	0.130326	-0.085963	-1.276543	-1.026853	0.365886	1.000
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
60989	248	0.508239	0.121028	0.152647	1.120924	0.971681	1.265227	0.799282	-0.682815	-0.501108	-0.619780	-0.269300	1.352772	-0.621083	-0.496962	-0.932685	1.264394	0.276756	1.001991	-0.945773	-0.865997	0.032
60990	248	0.519369	0.085226	0.111781	1.155888	0.996001	1.290563	0.797572	-0.691799	-0.467899	-0.626490	-0.271036	1.374981	-0.628321	-0.461072	-0.940386	1.280041	0.271074	1.020984	-0.972959	-0.886069	0.024
60991	248	0.530500	0.084878	0.112711	1.139622	1.001376	1.319797	0.800952	-0.681105	-0.485769	-0.616503	-0.262302	1.396260	-0.637931	-0.481980	-0.943458	1.285953	0.265512	1.031103	-0.964230	-0.911317	0.016
60992	248	0.541630	0.108114	0.128231	1.167516	1.015638	1.340998	0.807401	-0.684969	-0.519986	-0.616844	-0.264327	1.408435	-0.625176	-0.520504	-0.949622	1.296184	0.260067	1.040276	-0.988154	-0.921371	0.008
60993	248	0.552761	0.072631	0.087936	1.183535	1.036390	1.366365	0.805527	-0.695433	-0.482295	-0.620102	-0.266163	1.439875	-0.630817	-0.475905	-0.959527	1.302955	0.254736	1.046569	-1.007970	-0.944476	0.000

	0
0	22
1	39
2	107
3	75
4	149

	unit	time	os1	os2	s2	s3	s4	s6	s7	s8	s9	s10	s11	s12	s13	s14	s15	s16	s17	s20	s21	rul
0	1	1	-0.269665	0.414510	-1.296849	-0.374676	-2.451586	-1.721582	-1.273268	-1.618666	-1.364460	-1.411996	-1.811782	-1.258376	-1.676665	-1.186464	0.495988	-0.438767	-1.687027	-1.284064	-0.196332	125
1	1	2	-0.099150	0.284502	-1.322355	-0.578492	-2.249142	-1.615921	-1.265467	-1.285670	-1.214986	-3.428303	-2.110180	-1.292732	-1.328825	-1.211716	0.235910	-0.217168	-1.127919	-0.439244	-0.179037	125
2	1	3	0.348796	0.484108	-1.055020	-1.024428	-2.187763	-1.643934	-1.357749	-0.955073	-1.209620	-2.872028	-2.005613	-1.154556	-1.060450	-1.196568	0.457510	-0.143311	-1.266671	-0.957205	0.186380	125
3	1	4	0.572785	0.582399	-0.943568	-1.264165	-2.117617	-1.657937	-1.431287	-0.832917	-1.267886	-2.593948	-1.765094	-1.220575	-0.976190	-1.069855	0.437519	-0.106390	-1.336033	-0.847072	-0.352772	125
4	1	5	0.608639	0.641224	-0.946859	-1.378707	-1.928885	-1.641782	-1.432537	-0.928931	-1.295341	-2.159668	-1.818166	-1.232095	-0.976311	-1.054380	0.389832	-0.084244	-1.151784	-0.720828	-0.272035	125